Multisite Daily Streamflow Simulation With Time Irreversibility

Abstract

Synthetic daily streamflow generation requires a critical understanding of the underlying dynamics resulting from the inherent temporal asymmetry (time irreversibility) in the rising and falling limbs of the hydrograph. Most models considering the time irreversibility deal with single-site streamflow simulation. Addressing intersite dependencies is, however, crucial for interconnected stream networks. This study presents a time irreversibility dynamics-based multisite streamflow generating framework to simulate concurrent streamflow sequences at multiple sites in a basin. This framework explicitly takes into account the spatial correlation and time irreversible dynamics of streamflow. To demonstrate the utility and applicability, a few selected streamflow gauging stations in the Godavari River Basin, located in southern India, are considered. The proposed framework shows the ability to adequately generate multisite simulations capturing at-site statistics as well as intersite correlations for the case study. Furthermore, the approach ensures that the simulated flow values are not merely resampled from the historical data, uses the physical features of the hydrograph, and shows variability beyond that observed in the historical sequence. Such a rich variety of streamflow sequences can help water managers to investigate how existing water resources systems on interconnected stream networks will operate in scenarios which have not been observed in the historical record.

Multiscale Temporal Irreversibility of Streamflow and Its Stochastic Modelling

“…In this respect, Koutsoyiannis [3] proposed a model called AMA (Asymmetric Moving Average) in order to generate irreversible time series. Mathai and Mujumdar [8] have also built a model to simulate time irreversible streamflow at multiple sites. Multisitecorrelated streamflow states were generated, and then flow sequences were constructed by independently considering the ascension and recession limbs of the hydrograph at individual sites.…”

Use of streamflow indices to identify the catchment drivers of hydrographs

“…We first identify the hydrologic state of the stream (ascension and recession) (Mathai and Mujumdar, 2019). To determine the hydrologic state of a stream, increasing (wet) or decreasing (dry), on a given day, a time series of diurnal increments is extracted by differencing the original time series with its 1-day lagged time series.…”

“…McMillan (2021) presented a classification that differentiates between statistics and dynamics-based signatures and between signatures at different timescales. The relevance of time irreversibility (or temporal asymmetry) of streamflow variability on a daily scale has been emphasized in recent studies (Koutsoyiannis, 2020;Mathai and Mujumdar, 2019;Serinaldi and Kilsby, 2016). The disparity in physical mechanisms driving the hydrograph's rising and falling limbs (Fig.…”

Development of a Stepwise‐Clustered Multi‐Catchment Hydrological Model for Quantifying Interactions in Regional Climate‐Runoff Relationships

“…The Budyko framework was used to reveal the variations of runoff with climatic conditions (Abatzoglou & Ficklin, 2017;Roderick & Farquhar, 2011;Yang et al, 2014). More recently, Mathai and Mujumdar (2019) presented a time irreversibility dynamics-based multisite streamflow generating framework to simulate concurrent streamflow sequences at multiple sites in a basin. Conticello et al (2020) explored the synchronization and delay between circulation patterns and high streamflow events using multiple machine learning methods.…”